Simple Integration Research Articles

Advancements in porous framework materials for chemiresistive hydrogen sensing: exploring MOFs and COFs.

Hydrogen is a zero-emissive fuel and has immense potential to replace carbon-emitting fuels in the future. The development of efficient H2 sensors is essential for preventing hazardous situations and facilitating the widespread usage of hydrogen. Chemiresistors are popular gas sensors owing to their attractive properties such as fast response, miniaturization, simple integration with electronics and low cost. Traditionally, semiconducting metal oxides (SMOs) and Pd-based materials have been widely investigated for chemiresistive H2 sensing applications. However, issues such as limited selectivity and poor reliability still hinder their use in real-time applications. Recent advancements have explored metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), offering new perspectives and potential applications in this field. MOFs and COFs belong to the crystalline framework (CF) family of materials and are highly porous, designable materials with tunable pore surfaces featuring sites for H2 interactions. They exhibit good selectivity towards H2 with quick response/recovery times at relatively low temperatures compared to SMOs. Furthermore, they provide an additional advantage of sensing H2 in the absence of oxygen, even at high concentrations of H2. In this perspective article, we summarize recent advancements and challenges in the development of H2 sensors employing MOFs, COFs, and their hybrid composites as sensing elements. Additionally, we discuss our perspective on hybridizing MOFs/COFs with SMOs and other nanomaterials for the future development of advanced H2 sensors.

Biocompatible 2D Material Inks Enabled by Supramolecular Chemistry: From Synthesis to Applications.

ConspectusThe emergence of two-dimensional (2D) materials, such as graphene, transition-metal dichalcogenides (TMDs), and hexagonal boron nitride (h-BN), has sparked significant interest due to their unique physicochemical, optical, electrical, and mechanical properties. Furthermore, their atomically thin nature enables mechanical flexibility, high sensitivity, and simple integration onto flexible substrates, such as paper and plastic.The surface chemistry of a nanomaterial determines many of its properties, such as its chemical and catalytic activity. The electronic properties can also be modified by surface chemistry through changes in charge transfer or by the presence of surface states. Surface defects and functional groups can act as trap sites for excitons, hence affecting the optical properties. Furthermore, surface chemistry determines the stability and dispersibility of nanomaterials in colloidal dispersions as well as their biocompatibility and toxicity. In addition, the surface chemistry dictates how nanomaterials interact with biological systems, influencing cellular uptake, immune response, and biodistribution, to name a few examples. It is, therefore, crucial to be able to produce 2D materials with tunable surface chemistry to match target applications.Because of their dimensionality, 2D materials can be easily functionalized with noncovalent and covalent approaches. This review delves into the role of supramolecular chemistry, which is based on noncovalent interactions, in achieving stable and highly concentrated water-based dispersions of 2D materials with specific surface chemistry.In particular, we provide an overview of the recent progress made by our group in the field of solution-processed 2D materials produced by liquid-phase exfoliation with pyrene derivatives used as supramolecular receptors. We highlight the relationship between the structure of the pyrene derivative stabilizer and the concentration, stability, and lateral size and thickness distributions of the produced nanosheets. Subsequently, we give a short overview of the applications enabled by the supramolecular approach in printed electronics, sensing, bioelectronics, and in the biomedical field. We show that the careful design of the pyrene derivative enables us to achieve excellent stability of the material in the cellular medium, which is essential to accurately assess biological effects. We also highlight seminal case studies on the use of cationic graphene in the therapeutics of lysosomal storage disorders, and on the use of TMD nanosheets for trained immunity and as immune-compatible nanoplatforms, traceable at the single-cell and tissue (suborgan) levels.This Account aims to provide a comprehensive guide for readers on the potential of the supramolecular approach for the design of 2D material dispersions with tailored surface chemistry. This approach is expected to be extremely attractive for many applications, from tissue engineering to energy storage devices, so we hope that this Account will drive further efforts and advancements in this field by ultimately leading to the integration of solution-processed 2D materials made by supramolecular chemistry into practical applications.

On-detector power distribution for CMS-HGCAL: a busbar-based approach

An on-detector power distribution scheme for the High Granularity Calorimeter (HGCAL) Phase-2 upgrade of CMS is under development. This scheme is based on a heavy-copper flexible printed circuit board (FPC), allowing for an efficient use of the tight integration space, with minimal insulation overhead, excellent electrical and thermal performance and simplified integration, when compared with a wired solution. This work introduces the technology, how it allows the HGCAL challenges to be overcome, and the characterization studies. Prototypes testing are presented to validate the concept and quantify the manufacturability, electrical performance, and safety of the proposed solution.

The show must go on: a reliability assessment platform for resistive random access memory crossbars.

Resistive random access memory (ReRAM) holds promise for building computing-in-memory (CIM) architectures to execute machine learning (ML) applications. However, existing ReRAM technology faces challenges such as cell and cycle variability, read-disturb and limited endurance, necessitating improvements in devices, algorithms and applications. Understanding the behaviour of ReRAM technologies is crucial for advancement. Existing platforms can either only characterize single cells and do not support CIM operations, or lack a comprehensive software stack for simple system integration. This article introduces NeuroBreakoutBoard (NBB), a versatile, integrable and portable instrumentation platform for ReRAM crossbars. The platform features a software stack enabling experiments via Python from a host PC. In a case study, we demonstrate the capabilities of NBB by conducting diverse experiments on TiN/Ti/HfO2/TiN cells. Our results show that NBB can characterize individual cells and perform CIM operations with a relative measurement error below 2%.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.

Integration of paper-based analytical devices with digital microfluidics for colorimetric detection of creatinine.

Digital microfluidics (DMF) is a platform that enables the automated manipulation of individual droplets of sizes ranging from nanoliter to microliter and can be coupled with numerous techniques, including colorimetry. However, although the DMF electrode architecture is highly versatile, its integration with different analytical methods often requires either changes in sample access, top plate design, or the integration of supplementary equipment into the system. As an alternative to overcome these challenges, this study proposes a simple integration between paper-based analytical devices (PADs) and DMF for automated and eco-friendly sample processing aiming at the colorimetric detection of creatinine (CR, an important biomarker for kidney disease) in artificial urine. An optimized and selective Jaffé reaction was performed on the device, and the reaction products were delivered to the PAD, which was subsequently analyzed with a bench scanner. The optimal operational parameters on the DMF platform were a reaction time of 45 s with circular mixing and image capture after 5 min. Under optimized conditions, a linear behavior was obtained for creatinine concentrations ranging from 2 to 32 mg dL-1, with limits of detection and quantitation equal to 1.4 mg dL-1 and 2.0 mg dL-1, respectively. For the concentration range tested, the relative standard deviation varied from 2.5 to 11.0%, considering four measurements per concentration. CR-spiked synthetic urine samples were subjected to analysis via DMF-PAD and the spectrophotometric reference method. The concentrations of CR determined using both analytical techniques were close to the theoretical values, with the resultant standard deviations of 2-9% and 1-4% for DMF-PADs and spectrophotometry, respectively. Furthermore, the recovery values were within the acceptable range, with DMF-PADs yielding 96-108% and spectrophotometry producing 95-102%. Finally, the greenness of the DMF-PAD and spectrophotometry methods was evaluated using the Analytical Greenness (AGREE) metric software, in which 0.71 and 0.51 scores were obtained, respectively. This indicates that the proposed method presents a higher greenness level, mainly due to its miniaturized characteristics using a smaller volume of reagent and sample and the possibility of automation, thus reducing user exposure to potentially toxic substances. Therefore, the DMF-PADs demonstrated great potential for application in the clinical analysis of creatinine, aiding in routine tests by introducing an automated, simple, and environmentally friendly process.

Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.

Characterizing the negative triangularity reactor core operating space with integrated modeling

Abstract Negative triangularity (NT) has received renewed interest as a fusion reactor regime due to its beneficial power-handling properties, including low scrape-off layer power and a larger divertor wetted area that facilitates simple divertor integration. NT experiments have also demonstrated core performance on par with positive triangularity (PT) H-mode without edge-localized modes (ELMs), encouraging further study of an NT reactor core. In this work, we use integrated modeling to scope the operating space around two NT reactor strategies. The first is the high-field, compact fusion pilot plant concept MANTA (The MANTA Collaboration et al 2024 Plasma Phys. Control. Fusion 66 105006) and the second is a low field, high aspect ratio concept based on work by Medvedev et al (Medvedev et al 2015 Nucl. Fusion 55 063013). By integrating equilibrium, core transport, and edge ballooning instability models, we establish a range of operating points with less than 50 MW scrape-off layer power and fusion power comparable to positive triangularity (PT) H-mode reactor concepts. Heating and seeded impurities are leveraged to accomplish the same fusion performance and scrape-off layer exhaust power for various pressure edge boundary conditions. Scans over these pressure edge conditions accommodate any current uncertainty of the properties of the NT edge and show that the performance of an NT reactor will be extremely dependent on the edge pressure. The high-field case is found to enable lower scrape-off layer power because it is capable of reaching high fusion powers at a relatively compact size, which allows increased separatrix density without exceeding the Greenwald density limit. Adjustments in NT shaping exhibit small changes in fusion power, with an increase in fusion power density seen at weaker NT. Infinite-n ballooning instability models indicate that an NT reactor core can reach fusion powers comparable to leading PT H-mode reactor concepts while remaining ballooning-stable. Seeded krypton is leveraged to further lower scrape-off layer power since NT does not have a requirement to remain in H-mode while still maintaining high confinement. We contextualize the NT reactor operating space by comparing to popular PT H-mode reactor concepts, and find that NT exhibits competitive ELM-free performance with these concepts for a variety of edge conditions while maintaining relatively low scrape-off layer power.

Harnessing Ruthenium and Copper Catalysts for Formate Dehydrogenase Reactions.

Formic acid (HCOOH) is a promising source of hydrogen energy that can be used to produce hydrogen in a more economical and ecological way. Formic acid is a simple carboxylic acid with a high hydrogen concentration and is generally stable, making it useful as a hydrogen transporter. Catalytic dehydrogenation is usually used to extract hydrogen from formic acid; this process releases hydrogen gas and yields carbon dioxide as a byproduct. Comparing this technology to conventional hydrogen generation methods, there are several benefits, such as the utilization of the formic acid handling infrastructure already in place and the possibility of a simpler integration into different energy systems. Notwithstanding, several obstacles persist, including enhancing the effectiveness of the dehydrogenation procedure and reducing the ecological consequences of the correlated carbon dioxide discharges. Catalysts, reaction conditions, and carbon collection and utilization methodologies are all being researched further. The development of Ru and Cu-based catalysts for the catalytic breakdown of HCOOH into CO2 and H2 is the main topic of this account. Herein, the focus is on the kinetic studies of HCOOH dehydrogenation, encompassing mechanistic investigations that consider intermediate studies and DFT calculations.

Dynamic simulation of roll bond PVT solar collector under Simulink/Matlab

Abstract This paper presents an innovative methodology to investigate dynamic roll bond PVT solar systems under real-world operational weather conditions using the Simulink/Matlab environment. The method is based on the MPPT electrical model of the solar cell and the differential energy equations of PVT at each of its layers. The effect of PV operating temperature, mass flow rate, and insulation medium on overall PVT thermal performance are all thoroughly examined. Results reveal that the annual average power delivered, in terms of both heat and electricity, is 352.8 W and 34.08 W, respectively. Furthermore, the addition of an insulating layer could lead to a significant improvement in PVT overall effectiveness. The present work offers a simple and flexible integration tool for roll bond PVT system simulation.

Some Lower Bounds for a Double Integral Depending on Six Adaptable Functions

This paper is devoted to sharp lower bounds of a special double integral depending on six adaptable functions. The bounds obtained depend on simple integrals. Some connections with the famous Hilbert integral inequality and its variants are made. Several numerical examples illustrate the results.

Clinical staff attitudes towards opt-out consent for blood-borne virus screening in emergency departments in England.

Opt-out screening for blood-borne viruses (BBVs) in emergency departments (EDs) has been established in areas with a high prevalence of HIV diagnoses in England. This multi-site study explored the attitudes of healthcare workers (HCWs) towards BBV screening in EDs post-implementation. This was a cross-sectional electronic survey of HCWs. Between November 2023 and February 2024, HCWs across 33 EDs in England participating in opt-out BBV screening were invited to complete a survey about the feasibility and acceptability of screening, including the opt-out consent process. Factors independently associated with acceptability of opt-out screening were identified using multivariable logistic regression. Free-text responses were analysed thematically. Responses from 610 HCWs in 19 EDs were provided: 50.4% were nurses, 43.1% doctors, and 6.5% other healthcare professionals. Acceptability of the screening programme and opt-out consent was high (90.3% and 77.7%, respectively), with some variation between EDs. Acceptability of opt-out consent was greater among doctors than among other HCWs, and among HCWs who proactively discussed screening further with patients who opted out. However, 50.8% of HCWs felt that patients should be verbally reminded at blood draw, and 44.3% of HCWs wanted more training in discussing opt-out screening with patients. Free-text answers suggested changes to test-ordering systems, including simple integration of tick boxes to document whether patients opted out and to block repeated testing. There was substantial support from ED HCWs for routine opt-out ED BBV screening, including opt-out consent. Key areas suggested for improvement included changes to test-ordering systems and additional training for HCWs. Frequent preference for verbal reminders at the point of blood draw suggests continued HIV testing exceptionalism.

Static and Transient Response Analysis of Arbitrary Laminated Composite and Sandwich Shells by the FSDT Meshless Method

This paper, for the first time, presents a novel meshless method based on the first-order shear deformation theory (FSDT) and moving-least squares (MLS) approximation to carry out static and transient response analysis on arbitrary laminated and sandwich shell structures. The novelty of the method lies in the implementation of MLS in the curvilinear shell formulation, and the derivation of meshless governing equations on the static and transient response of arbitrary laminated and sandwich shells according to the principle of minimum potential energy and Hamilton’s principle, respectively. The concept of a Convected coordinate system is adopted to deal with arbitrary curvilinear surfaces. Both the position and displacement vectors of shells are approximated by MLS, which is conceptually the same procedure as that in the isoparametric finite element method (FEM). The numerical integration of the stiffness matrices is conducted by the simple Gaussian integral in the Convected coordinate system. Because the moving-least squares (MLS) approximation does not satisfy the Kronecker delta condition, the full transformation method is used to modify the meshless governing equations. Numerical examples are calculated to investigate the static and transient response analysis of laminated and sandwich structures with different geometrical shell shapes, including square plates, spherical shells, doubly-curved shells, and cylindrical shells. The numerical tests show that the proposed method is reliable, and robust and produces accurate results compared to the analytical and numerical solutions taken from the previous literature.

On the representation and computational aspects of the distribution of a linear combination of independent noncentral chi-squared random variables

This article presents two new results relevant to a linear combination of χ2 random variables. The first result expresses the cumulative distribution function as a simple multiple of a certain tilted density. The second result shows that in important cases the inversion integral for the density may be expressed as a sum of relatively simple real integrals which, using suitable numerical methods, are straightforward to compute quickly and accurately.

Study on the design and application of the three-electrode ionization particle sensor

Accurate detection is the foundation of haze governance. The existing particle concentration detection methods and instruments have many shortcomings, such as complex structure, high cost, easy blockage, short life, and significant impact of environmental changes. Therefore, this paper proposes a silicon micropillar three-electrode ionization particle sensor based on the gas discharge principle and studies the sensor's sensitivity to PM2.5 particles. Three-electrode comprises a cathode with micro silicon pillars grown on the surface, an extracting electrode, and a collecting electrode. It shows that the cathode current of the sensor increases linearly with extraction voltage and nonlinearly with temperature. As the cathode material of the sensor, the silicon micropillar is compared with the carbon nanotube to solve the burn problem of the nanotube rising from positive ions bombardment during gas discharge. The collecting current of the sensor shows a decreasing trend with the increase in particle concentration. The collecting current and sensitivity of the sensor are much higher in radio frequency excitation than in direct current excitation. The sensor developed in this paper displays the advantages of simple technology, high integration, low cost, and high sensitivity, and it exhibits great application potential.

Leveraging Flask API and Machine Learning to Forecast Multiple Diseases

The study described in this abstract used machine learning to predict several diseases, and it integrated the model into a Flask API. A user-friendly platform for disease prediction based on patient symptoms and medical history was the goal of the paper. A number of strategies were used to optimize the predictive performance once the machine learning model had been trained on a sizable dataset of patient records. The model was made accessible as a web service through the usage of the Flask API, enabling simple deployment and integration into current healthcare systems. The study's findings demonstrated that the model could accurately forecast diseases with a high degree of precision, and the Flask API gave healthcare professionals an easy way to use the model in real-world settings. The study underscores the significance of developing open and user-friendly platforms for healthcare applications and shows the potential of machine learning and online APIs in improving disease detection. Additionally, the study discovered that the model could correctly identify disorders even in the absence of conclusive diagnostic tests.

Boolean Computation in Single-Transistor Neuron.

Brain neurons exhibit far more sophisticated and powerful information-processing capabilities than the simple integrators commonly modeled in neuromorphic computing. A biological neuron can in fact efficiently perform Boolean algebra, including linear nonseparable operations. Traditional logic circuits require more than a dozen transistors combined as NOT, AND, and OR gates to implement XOR. Lacking biological competency, artificial neural networks require multilayered solutions to exercise XOR operation. Here, it is shown that a single-transistor neuron, harnessing the intrinsic ambipolarity of graphene and ionic filamentary dynamics, can enable in situ reconfigurable multiple Boolean operations from linear separable to linear nonseparable in an ultra-compact design. By leveraging the spatiotemporal integration of inputs, bio-realistic spiking-dependent Boolean computation is fully realized, rivaling the efficiency of a human brain. Furthermore, a soft-XOR-based neural network via algorithm-hardware co-design, showcasing substantial performance improvement, is demonstrated. These results demonstrate how the artificial neuron, in the ultra-compact form of a single transistor, may function as a powerful platform for Boolean operations. These findings are anticipated to be a starting point for implementing more sophisticated computations at the individual transistor neuron level, leading to super-scalable neural networks for resource-efficient brain-inspired information processing.

Flexible electronic-photonic 3D integration from ultrathin polymer chiplets

Integrating flexible electronics and photonics can create revolutionary technologies, but combining these components on a single polymer device has been difficult, particularly for high-volume manufacturing. Here, we present a robust chiplet-level heterogeneous integration of polymer-based circuits (CHIP), where ultrathin polymer electronic and optoelectronic chiplets are vertically bonded at room temperature and shaped into application-specific forms with monolithic Input/Output (I/O). This process was used to develop a flexible 3D integrated optrode with high-density microelectrodes for electrical recording, micro light-emitting diodes (μLEDs) for optogenetic stimulation, temperature sensors for bio-safe operations, and shielding designs to prevent optoelectronic artifacts. CHIP enables simple, high-yield, and scalable 3D integration, double-sided area utilization, and miniaturization of connection I/O. Systematic characterization demonstrated the scheme’s success and also identified frequency-dependent origins of optoelectronic artifacts. We envision CHIP being applied to numerous polymer-based devices for a wide range of applications.

Synergistic effects of MXene support and cobalt salts in dye-sensitized photocatalytic hydrogen generation

This study introduces an approach to photocatalytic hydrogen production through a dye-sensitized photocatalytic system employing MXene (Ti3C2Tx) and a non-noble cobalt metal catalyst. It was demonstrated that simple integration of eosin Y, Ti3C2Tx and cobalt salt (CoSO4) significantly enhances the photocatalytic hydrogen evolution, outperforming the corresponding system that does not include Ti3C2Tx. The key to this enhanced performance lies in the unique properties of MXene material, which facilitates effective electron transfer and acts as a support for dye and catalyst. The successful well-dispersed decoration of small (2–3 nm) cobalt-based nanoparticles on the Ti3C2Tx sheet via in situ photodeposition was confirmed by transmission electron microscopy (TEM) and X-ray Photoelectron Spectroscopy (XPS). The optimal hydrogen production rates were achieved with a Co to Ti3C2Tx mass ratio of 15%. This optimized interaction results in a hydrogen evolution rate of 40.1 mmol h−1 g−1 and an apparent quantum efficiency (AQE) of 35.4% at 505 nm. These findings highlight the potential of Ti3C2Tx as a versatile component in photocatalytic systems, particularly effective when paired with economically viable, earth-abundant catalysts like cobalt.

Target-field design of surface permanent magnets

We present a target-field approach to analytically design magnetic fields using permanent magnets. We assume that their magnetization is bound to a two-dimensional surface and is composed of a complete basis of surface modes. By posing the Poisson’s equation relating the magnetic scalar potential to the magnetization using Green’s functions, we derive simple integrals that determine the magnetic field generated by each mode. This approach is demonstrated by deriving the governing integrals for optimizing axial magnetization on cylindrical and circular-planar surfaces. We approximate the governing integrals numerically and implement them into a regularized least-squares optimization routine to design permanent magnets that generate uniform axial and transverse target magnetic fields. The resulting uniform axial magnetic field profiles demonstrate more than a tenfold increase in uniformity across equivalent target regions compared to the field generated by an optimally separated axially magnetized pair of rings, as validated using finite element method simulations. We use a simple example to examine how two-dimensional surface magnetization profiles can be emulated using thin three-dimensional volumes and determine how many discrete intervals are required to accurately approximate a continuously varying surface pattern. Magnets designed using our approach may enable higher-quality bias fields for electric machines, nuclear fusion, fundamental physics, magnetic trapping, and beyond. Published by the American Physical Society 2024

Sparse-spike seismic inversion with semismooth newton algorithm solver

Seismic prospecting has been widely used in the exploration and development of underground geological resources, such as mineral products (e.x., coal, uranium deposit), oil and gas, groundwater, and so forth. Seismic impedance is a physical characteristic parameter of underground formation, which can be used in lithologic classification, rock characterization, stratigraphic correlation, and further mineral reservoir prediction, reserve estimation, and so forth. To estimate impedance from seismic data, one must perform reflectivity series inversion first. Under a simple exponential integration transformation, the reflectivity series can give the final estimated impedance. Sparse-spike seismic inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on reflectivity series. From sparse reflectivity series, the final estimated impedance has blocky features to make formation interfaces and geological edges precise, which is very important to accurately delineate the distribution range of mineral resources. The development of sparse-spike seismic inversion is still facing major challenges of fast optimization algorithms in real-life application, especially for massive seismic data in 3D case. Semismooth Newton algorithm (SNA), which is a second order mehtod and has super-linear, even quadratic convergence rate, is used to solve sparse-spike seismic inversion. The proposed algorithm has been compared with common used algorithms through a synthetic seismic trace and a 3D real seismic data volume. The results show that the proposed algorithm has faster convergence rate and fewer computation time. It provides a new effective algorithm to solve sparse-spike seismic inversion.